Hybrid First-Principle/Neural Network Correlations for Thermoelectric Transport Coefficients in Gold-Silver Solutions from Bulk to Nanometer Scale
Bibliographic record
Abstract
State of art estimation methods were revisited to build hybrid first-principle/artificial neural network correlations to capture the impact of solute concentration, specimen sizes down to nanometer scale, and electron and phonon temperatures in (non)equilibrium for the electric and thermal transport coefficients in gold-silver mixtures at temperatures above the metals Debye temperatures. Deviations with respect to Matthiessen’s additivity rule of both electric and electronic thermal transport coefficients were approximated by means of two neural network correlations as a function of silver atom fraction and temperature. The hybrid approach was confronted and validated against a large repository of data recommended for gold-silver transport properties encompassing pure metals and the full binary-solution composition range. Sensitivity of electric and thermal conductivities in gold-silver mixtures to electron and phonon temperatures, nanoparticle sizes and silver contamination was also discussed in the developed frame. The developed correlations will be useful for estimation of transport properties in areas as diverse as catalysis, electrochemical dissolution and gold nanomaterial synthesis
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".